Head CT overuse in children with a mild traumatic brain injury within two Canadian emergency departments
Bibliographic record
Abstract
BACKGROUND: The validated Pediatric Emergency Care Applied Network (PECARN) rule helps determine the relevance of a head computerized tomography (CT) for children with mild traumatic brain injury (mTBI). We sought to estimate the potential overuse of head CT within two Canadian emergency departments (EDs). METHODS: We conducted a retrospective chart review of children seen in 2016 in a paediatric Level I (site 1) and a general Level II (site 2) trauma centre. We reviewed charts to determine the appropriateness of head CT use according to the PECARN rule in a random subset of children presenting with head trauma. Simple descriptive statistics were applied. RESULTS: One thousand five hundred and forty-six eligible patients younger than 17 years consulted during the study period. Of the 203 randomly selected cases per setting, 16 (7.9%) and 24 (12%), respectively from sites 1 and 2 had a head CT performed. Based on the PECARN rule, we estimated the overuse for the younger group (<2 years) to be below 3% for both hospitals without significant difference between them. For the older group (≥2 years), the overuse rate was higher at site 2 (9.3%, 95% confidence interval [CI]: 4.8 to 17% versus 1.2%, 95% CI: 0.2 to 6.5%, P=0.03). CONCLUSION: Both EDs demonstrated overuse rates below 10% although it was higher for the older group at site 2. Such low rates can potentially be explained by the university affiliation of both hospitals and by two Canadian organizations working to raise awareness among physicians about the overuse of diagnostic tools and dangers inherent to radiation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".